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metadata
license: cc-by-4.0
language:
  - en
task_categories:
  - tabular-classification
  - tabular-regression
  - time-series-forecasting
multilinguality: monolingual
size_categories:
  - 1K<n<10K
tags:
  - tabular
  - europe
  - ilostat
  - workers-in-stem-occupations
  - ilo
  - labour
  - employment
pretty_name: >-
  Employment in STEM occupations by sex and place of birth (thousands) | Europe
  (ILOSTAT)

Employment in STEM occupations by sex and place of birth (thousands) | Europe (ILOSTAT)

🇪🇺 1,382 observations · 13 Europe countries · 2008–2025 · Repackaged by Electric Sheep Europe

rows countries years indicators license

TL;DR

This dataset contains 1,382 observations of Workers in STEM occupations data across 13 Europe countries, spanning 2008–2025, covering 1 distinct indicators.

About the source

ILOSTAT is the ILO's central statistics database, the leading global source for labour statistics. It compiles indicators across employment, unemployment, wages, working time, child labour, informal economy, social protection, occupational injuries, and SDG decent work targets — drawing on national labour force surveys, household income surveys, establishment surveys, and administrative records. Coverage spans 200+ economies, with the ILO's Department of Statistics responsible for harmonisation.

  • Source: ILOSTAT
  • Publisher: International Labour Organization (ILO)
  • License: cc-by-4.0
  • Topic: Workers in STEM occupations

Methodology

Data pulled directly from the ILOSTAT REST API at https://rplumber.ilo.org/data/indicator?id=EMP_STEM_SEX_CBR_NB and filtered to Europe ISO3 country codes. ILOSTAT harmonises raw survey microdata using ICLS (International Conference of Labour Statisticians) definitions; sources are flagged in the source.label column for traceability.

Geographic coverage

13 Europe countries · top rows shown below, sorted by row count:

Country Rows First year Last year
GRC 162 2008 2025
CHE 161 2009 2025
FRA 144 2011 2024
AUT 135 2011 2025
PRT 135 2011 2025
CZE 128 2011 2024
SVK 117 2012 2023
BIH 113 2012 2024
SRB 81 2011 2019
GBR 76 2018 2025
ITA 63 2014 2020
ALB 58 2016 2024
ESP 9 2011 2011

Indicators (sample)

  • EMP_STEM_SEX_CBR_NB — Employment in STEM occupations by sex and place of birth (thousands)

Schema

Column Type Description Example
ref_area string ISO 3166-1 alpha-3 country code ALB
ref_area.label string Country name in English Albania
source string ILOSTAT source code (e.g. labour force survey) BA:480
source.label string Source name in English LFS - Labour Force Survey
indicator string ILOSTAT indicator code EMP_STEM_SEX_CBR_NB
indicator.label string Indicator name in English Employment in STEM occupations by sex…
sex string Disaggregation by sex (SEX_T = total, SEX_M = male, SEX_F = female) SEX_T
sex.label string Total
classif1 string First classification variable (age, education, status, etc.) CBR_BIR_TOTAL
classif1.label string Place of birth: Total
time int64 Observation year 2024
obs_value float64 Observed indicator value (unit varies — see indicator definition) 70.394
obs_status string Observation status flag (e.g. provisional, unreliable) U
obs_status.label string Unreliable
note_indicator string I11:264
note_indicator.label string Break in series: Methodology revised
note_source string R1:3513
note_source.label string Repository: ILO-STATISTICS - Micro da…

Disaggregation dimensions

The following columns provide disaggregation dimensions:

  • sex (3 unique values): SEX_T, SEX_M, SEX_F

Data quality & caveats

  • Data is annual frequency. Some indicators also publish monthly or quarterly series — those are not included here.
  • When an indicator has multiple sources for the same country×year, the ILO-selected 'best source' is used.
  • Disaggregation columns (sex, classif1, classif2) are non-null only when the indicator publishes that breakdown.

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepeurope/europe-ilo-emp-stem-sex-cbr-nb-employment-in-stem-occupations-by-sex-and-place-of")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

germany = df[df["ref_area"] == "DEU"]

Time-series for a single indicator

sample = (df[df["indicator"] == "EMP_STEM_SEX_CBR_NB"]
          .sort_values("time"))
sample.plot(x="time", y="obs_value", title="EMP_STEM_SEX_CBR_NB")

Pivot to country × year matrix

matrix = (df[df["indicator"] == "EMP_STEM_SEX_CBR_NB"]
          .pivot_table(index="time", columns="ref_area", values="obs_value"))
print(matrix.tail())

Citation

@misc{europe_ilo_emp_stem_sex_cbr_nb_employment_in_stem_occupations_by_sex_and_place_of_2025,
  title        = {Employment in STEM occupations by sex and place of birth (thousands) | Europe (ILOSTAT)},
  author       = {International Labour Organization (ILO)},
  year         = {2025},
  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_STEM_SEX_CBR_NB},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Europe},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-ilo-emp-stem-sex-cbr-nb-employment-in-stem-occupations-by-sex-and-place-of}}
}

License

Released under cc-by-4.0.

Original data © International Labour Organization (ILO). When using this dataset, please cite both the original source above and the Electric Sheep Europe repackaging.

About Electric Sheep

Electric Sheep Europe is part of the Electric Sheep mission: a unified, ML-ready data layer for Europe on HuggingFace. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use load_dataset() to start working in seconds.

Browse the full collection: huggingface.co/electricsheepeurope


Provenance: ingested 2026-05-28 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_STEM_SEX_CBR_NB